Complex influence propagation based on trust-aware dynamic linear threshold models

Abstract To properly capture the complexity of influence propagation phenomena in real-world contexts, such as those related to viral marketing and misinformation spread, information diffusion models should fulfill a number of requirements. These include accounting for several dynamic aspects in the propagation (e.g., latency, time horizon), dealing with multiple cascades of information that might occur competitively, accounting for the contingencies that lead a user to change her/his adoption of one or alternative information items, and leveraging trust/distrust in the users' relationships and its effect of influence on the users' decisions. To the best of our knowledge, no diffusion model unifying all of the above requirements has been developed so far. In this work, we address such a challenge and propose a novel class of diffusion models, inspired by the classic linear threshold model, which are designed to deal with trust-aware, non-competitive as well as competitive time-varying propagation scenarios. Our theoretical inspection of the proposed models unveils important findings on the relations with existing linear threshold models for which properties are known about whether monotonicity and submodularity hold for the corresponding activation function. We also propose strategies for the selection of the initial spreaders of the propagation process, for both non-competitive and competitive influence propagation tasks, whose goal is to mimic contexts of misinformation spread. Our extensive experimental evaluation, which was conducted on publicly available networks and included comparison with competing methods, provides evidence on the meaningfulness and uniqueness of our models

A Computational Model of Trust Based on Dynamic Interaction in the Stack Overflow Community

A member’s reputation in an online community is a quantified representation of their trustworthiness within the community. Reputation is calculated using rules-based algorithms which are primarily tied to the upvotes or downvotes a member receives on posts. The main drawback of this form of reputation calculation is the inability to consider dynamic factors such as a member’s activity (or inactivity) within the community. The research involves the construction of dynamic mathematical models to calculate reputation and then determine to what extent these results compare with rules-based models. This research begins with exploratory research of the existing corpus of knowledge. Constructive research in the building of mathematical dynamic models and then empirical research to determine the effectiveness of the models. Data collected from the Stack Overflow (SO) database is used by models to calculate a rule-based and dynamic member reputation and then using statistical correlation testing methods (i.e., Pearson and Spearman) to determine the extent of the relationship. Statistically significant results with moderate relationship size were found from correlation testing between rules-based and dynamic temporal models. The significance of the research and its conclusion that dynamic and temporal models can indeed produce results comparative to that of subjective vote-based systems is important in the context of building trust in online communities. Developing models to determine reputation in online communities based upon member post and comment activity avoids the potential drawbacks associated with vote-based reputation systems

REPUTATION COMPUTATION IN SOCIAL NETWORKS AND ITS APPLICATIONS

This thesis focuses on a quantification of reputation and presents models which compute reputation within networked environments. Reputation manifests past behaviors of users and helps others to predict behaviors of users and therefore reduce risks in future interactions. There are two approaches in computing reputation on networks- namely, the macro-level approach and the micro-level approach. A macro-level assumes that there exists a computing entity outside of a given network who can observe the entire network including degree distributions and relationships among nodes. In a micro-level approach, the entity is one of the nodes in a network and therefore can only observe the information local to itself, such as its own neighbors behaviors. In particular, we study reputation computation algorithms in online distributed environments such as social networks and develop reputation computation algorithms to address limitations of existing models. We analyze and discuss some properties of reputation values of a large number of agents including power-law distribution and their diffusion property. Computing reputation of another within a network requires knowledge of degrees of its neighbors. We develop an algorithm for estimating degrees of each neighbor. The algorithm considers observations associated with neighbors as a Bernoulli trial and repeatedly estimate degrees of neighbors as a new observation occurs. We experimentally show that the algorithm can compute the degrees of neighbors more accurately than a simple counting of observations. Finally, we design a bayesian reputation game where reputation is used as payoffs. The game theoretic view of reputation computation reflects another level of reality in which all agents are rational in sharing reputation information of others. An interesting behavior of agents within such a game theoretic environment is that cooperation- i.e., sharing true reputation information- emerges without an explicit punishment mechanism nor a direct reward mechanisms